Introducing machine learning model to response surface methodology for biosorption of methylene blue dye using Triticum aestivum biomass

A major environmental problem on a global scale is the contamination of water by dyes, particularly from industrial effluents. Consequently, wastewater treatment from various industrial wastes is crucial to restoring environmental quality. Dye is an important class of organic pollutants that are considered harmful to both people and aquatic habitats. The textile industry has become more interested in agricultural-based adsorbents, particularly in adsorption. The biosorption of Methylene blue (MB) dye from aqueous solutions by the wheat straw (T. aestivum) biomass was evaluated in this study. The biosorption process parameters were optimized using the response surface methodology (RSM) approach with a face-centred central composite design (FCCCD). Using a 10 mg/L concentration MB dye, 1.5 mg of biomass, an initial pH of 6, and a contact time of 60 min at 25 °C, the maximum MB dye removal percentages (96%) were obtained. Artificial neural network (ANN) modelling techniques are also employed to stimulate and validate the process, and their efficacy and ability to predict the reaction (removal efficiency) were assessed. The existence of functional groups, which are important binding sites involved in the process of MB biosorption, was demonstrated using Fourier Transform Infrared Spectroscopy (FTIR) spectra. Moreover, a scan electron microscope (SEM) revealed that fresh, shiny particles had been absorbed on the surface of the T. aestivum following the biosorption procedure. The bio-removal of MB from wastewater effluents has been demonstrated to be possible using T. aestivum biomass as a biosorbent. It is also a promising biosorbent that is economical, environmentally friendly, biodegradable, and cost-effective.

. A schematic diagram of biosorption study. www.nature.com/scientificreports/ kinetics, the test solutions' starting colour density and response time were changed. With the use of a pH metre (Make: HANNA instruments, USA, Model: HI 991001), the pH of the dye solution was changed. To determine how temperature affects different thermodynamic parameters, biosorption research was conducted using diluted HCl or NaOH solutions. Biosorption levels (q t ) at time t (mg/g) were calculated using Eq. (1) 31 C t (mg/L) indicates the overall dye concentration, C o (mg/L) the initial dye concentration, V the solution volume (L), and W the dry biosorbent mass (g). In order to determine the amount of biosorption at equilibrium, q e (mg/g), was shown in Eq. (2) In this equation, C e is the equilibrium dye concentration (in mg/L). To respond to the RSM investigation, the dye removal percentage was calculated using Eq. (3) Design of experiments using RSM. Because only one variable is changed while the other variables are kept constant in a standard experiment, the researcher ignores the synergistic effect of the components. A variety of optimization methodologies have been developed in operational analysis over the years, resulting in a long history of optimization studies 32 . RSM is a methodical statistical methodology that improves the agreement of the minimal test runs when evaluating the relationship between design responses and factors 33 . The quadrilateral design is provided because the CCD contains only a subset of the experiments required for the five-step factorial and provides schemes with the required statistical properties 34,35 .
The number of experiments needed for CCD design can be determined by where c is the number of centre-point replicas, n is the number of numerical components, and N is the total number of experiments 25 . The graphical analysis, regression analysis, and experimental design were all carried out using software from Stat-Ease Inc. known as Design Expert. A total of 30 trials were designed, each containing six repetitions of the centre points, eight replications of the axial points, and sixteen replications of the cubical points, in accordance with Eq. (4). Regression equations were used to determine the variables' ideal circumstances. Using a four-point combination of four variables and three phases, the maximum organic sorbent dosage, pH, initial metal ion concentration, and temperature were all calculated 35 . This design was chosen because it met most of the criteria for optimizing bio-absorption studies 26 . Finding ideal process working conditions to meet performance standards is the primary goal of RSM.
Modelling of the biosorption isotherm. We used the biosorption facts of MB on T. aestivum to solve the isotherm equations. According to the Langmuir isotherm, neither the target molecules nor the adsorbent surface will ever interact. The model also includes a restricted number of energetic websites, which are typically organised in a monolayer 36 . Langmuir isotherms may be used to simulate the biosorption process. q e is the quantity of dye absorbed at equilibrium in mg and q max is the maximum quantity of dye that can be absorbed via means of biomass in mg, C e is the equilibrium MB awareness expressed in mg/L, whilst b is the Langmuir isotherm constant. Instead, the Langmuir equation's linearized form can be shown as follows.
(1) www.nature.com/scientificreports/ C e denotes for equilibrium concentration of MB (mg/L), q e for the quantity of MB absorbed at equilibrium (mg/g), and q m for maximum/monolayer biosorption capacity (mg/g) respectively. The basic characteristics of the Langmuir isotherm are described by a non-dimensional dissociation constant, R L .
Biosorption kinetics modelling. The experimental data on biosorption in this study were optimised using pseudo-first-order. This kinetics study used different dye concentrations (C o = 10, 20, 30, 40, and 50 mg/L) to evaluate the kinetics for five to ninety minutes. The basic description of the biosorption rate determined by biosorption capacity is given below in accordance with Lagergren's first-order rate equation. Typically, a linear expression for this rate is used 37 .
The quantities of MB adsorbed on T. aestivum at equilibrium (q e ) and at any time (q t ), respectively; K 1 (min −1 ) is the pseudo-first-order biosorption rate constant (q t ). H o suggested an expression-based rate-based kinetic model with quadratic coefficients has been put forth for the biosorption of dissociated metal ions (adsorbents) in coal particles. The adsorbent's biosorption capacity is consistent with this model 38 . This model is consistent with the adsorbent's capacity for biosorption. The model presents a pseudo-quadratic rate equation and aims to separate the kinetics of the biomass concentration-based quadratic rate equation from the solvent concentrationbased data. The pseudo-quadratic model's linear form is as follows in Eq. (9) T. aestivum absorbs MB dye at equilibrium (mg/g) and at any time, which is designated as q e and q t , respectively. The equilibrium rate constant for pseudo-second-order biosorption is K 2 . To determine how they would affect H o 's proposed pseudo-second-order model of biosorption kinetics, the test solutions' initial colour density and reaction time were altered.

Biosorption thermodynamics studies. Thermodynamic parameters include entropy ( S), changes in
Gibb's free energy ( G), and enthalpy ( H), of biosorption at various temperatures for MB dye onto the T. aestivum 29 . Five different temperatures were used to investigate the impact of temperature on batch-by-batch tests of MB dye on T. aestivum. The following diagram iillustrate the thermodymanics parameters influences on G variation during the biosorption process 39 . The slope and intercept of the following function were used to calculate the change in entropy and enthalpy during the biosorption process.

ANN-based predictive modelling.
Few studies have previously used machine learning (ANN) modelling to forecast dye removal of MB performance 40 . The neurons that make up an ANN are highly coupled processing units that have summing junction and transfer functions. ANN modelling, in contrast to RSM, includes an input (factors), target (experimental response), and output (predicted response). The input layer (representing independent variables), output layer (representing dependent variables), and hidden layers that link inputs with outputs are the layers in which the artificial neurons are placed 41 . Figure 2 illustrates the pattern of neuronal.

Results and discussion
Response surface methodology. The highest dye removal observed and anticipated MB biosorption values, and the matrix of experimental design are listed in Table 4. In 60 min, 30 tests in total were conducted. T. aestivum had the highest dye removal rate (96%) compared to other combinations with 1.5 mg of biosorbent, 10 mg/L dye solution, pH 6, and a temperature of 25 °C. The link between the independent variables chosen and the biosorption of the MB dye is described by regression equations, which are used to express RSM. For this investigation, the regression equation is expressed in terms of coded values is shown as Eq. (11).
A, B, C, and D are the coded variables used in this RSM investigation. To forecast how each element will react to different phases, the equation can be utilised in conjunction with the coded variable. The standard notation for superior and subordinate status is + 1 and 1, respectively. Using this coding equation, the relative effects of the variables are ascertained after comparing the coefficients of the factors. Figure 3 illustrates that the predicted value  Table 4. Actual value and projected MB removal are included in the experimental design matrix. Annotation: A (biosorbent dose), B (dye solution pH), C (initial dye concentration), and D (temperature).

Dye removal (%)
Actual ANOVA analysis. ANOVA analysis used all the experimental findings for the full factorial response variable that was duplicated at the central and axial points (Table 5). A significant quadratic model contribution is shown in the ANOVA findings in Table 5 with a p-value of less than 0.01. The significant model in the current investigation is shown by the sample F-value of 193.32. This huge F-value may be caused by noise with a mere 0.01% probability. The values obtained using R 2 = 0.9945 show a strong correlation between the experimental data currently available and the predicted values of the model put forth to describe the property of the polynomial model. This correlation is described by the calculation of the coefficient, the mean deviation across the model described, and the values themselves. The results with R 2 = 0.9945 show that there is a strong correlation between the experimental data that is currently available and the predicted values of the model that is suggested to reflect the property of the polynomial model. The determination of the coefficient, the mean deviation throughout the described model, and the value all demonstrate this link. The value of F is 4.54 shows that there may be a 5.43% risk that the considerable prevalence of Fit F-value deficiency is because of noise, and the absence of Fit is not statistically significant. Response surface plots show the MB biosorption efficiency (%) response to common parameters based on most values of alternative parameters for a certain set of components is shown in Fig. 4a-f. These 3D plots' curves demonstrate how the process variables interact. The optimum scenario and interacting outcomes of the four evaluated factors are shown in the 3D aspect plots in Fig. 4a-f.
Effect of biosorbent dose. The availability and cost of biomass are the main deciding considerations when adopting it for large-scale industrial purposes. Biomass is one of the most exciting categories of biosorbents 39 . In terms of getting rid of heavy metals from wastewater, agricultural biomass has a whole lot of benefits, which include being a cost-powerful renewable natural biomass, having an excessive metal elimination efficiency, having an excessive ability for absorption, and being capable of regenerating and reusing the biomass 42 .    44 . The reality that the dye's biosorption per cent decreases as biomass attention increases demonstrates that the wide variety of dye molecules required to absolutely cowl all the lively adsorption sites in the biomass at excessive sorbent doses is insufficient 1 .
Effect of MB concentrations. The relationship between the biosorbent dose and the concentration of MB dye is shown in Fig. 4a. The MB removal percentages increase with the increase of biosorbent dose and dye concentration. The biosorption process is also influenced by the initial MB concentrations. Growing the preliminary dye concentrations usually causes growth within the elimination percentage. The biosorption quantity of dye on the surface of adsorbents increases as the initial concentration of MB increases 45 .
Effect of initial pH. The biosorption procedure may be motivated with the aid of using numerous variables, along with pH, preliminary concentration, and biosorbent dosage. Figure 4c describes the association between the pH and the temperature. While the initial pH level, MB concentration, and contact duration were retained at their zero levels, the three-dimensional surface plots (3D) in Fig. 4c show the simultaneous effects of pH and temperature on MB removal (%), respectively. The process of contaminant biosorption has been discovered to be most affected, among other things, by the initial pH level. pH levels influence a variety of processes, including the chemistry of metal solutions, the activity of functional groups in biomass, and the net charge on the surface of sorbent cells. Heavy metal ions and H + may compete with one another for cellular active sites on the surface of biosorbent cells since the biosorption method for significant metals is usually potential of hydrogen ion concentration dependent 46 . According to the study of experimental findings, the T. aestivum biomass can more efficaciously soak up the MB dye because the pH rises, with maximum biosorption happening at approximately pH 8. The T. aestivum surface appearing as a biosorbent and the protonation and deprotonation of the MB dye can each be used to provide an explanation for the outcome.
Effect of temperature. The biosorption process sensitivity to temperature can be used to determine a biosorbent sorption capacity. The impact of temperature on the removal of Basic Blue 41(BB41) through effective microorganism-primarily based total leaf compost was assessed at various temperatures between 25 and 45 °C 47 . The outcomes of the experiment showed that a rise in temperature would result in a greater capacity for dye sorption (Fig. 4b). Figure 3e demonstrates that the slightly increasing the concentration at lower temperature the efficiency of dye removal also increases. Researchers have found that increasing temperatures increase the rate of solute diffusion, which has a significant impact on the sorbent's ability to absorb solutes 48 . However, the impact www.nature.com/scientificreports/ of temperature on biosorption is quite delicate and might be slightly increased at lower temperatures. The ability of the dye molecules to sustain contact with the biosorbent surface sites and the expansion of pore size with rising temperature were cited as the causes of this outcome. In general, a rise in temperature accelerates the rate of solute diffusion, which has a significant impact on the ability of biosorbents to bind to solutes 48 . Fig. 5A. According to Table 6, The Langmuir isotherm's determined correlation coefficients were 0.9381. The biosorption's deviation from linearity is considered when calculating the second Langmuir constant, R L . In the current investigation, the equilibrium value of the dimensionless factor value, R L , which ranges from 0 to 1, was 0.062 (Table 6), indicating favourable biosorption. That confirmed that T. aestivum and MB had favourable biosorption (Fig. 5A). Figure 5B illustrates the values of 1/n and K f determined from the intercept and slope of the linear plot of ln q e versus ln C e 49 . The desired constants are provided with the regression equation as shown in Table 6. The favourable nature of biosorption was proved by the fact that n is between 0 and 1 50 . The Langmuir and Freundlich biosorption isotherms best explain the equilibrium results, demonstrating that monolayer formation mediates biosorption on a homogeneous surface. Figure 5B shows a linear fit of the Freundlich equation using a line with an intercept of ln K f and a slope of n 49 .

Isotherms model for biosorption. A fitting result of a linear line with a (C e /q) intercept to the Langmuir equation is displayed as (C e /q) versus (C e ) shown in
Kinetic studies. The first-order kinetics' calculated K 1 , q e , and R 2 values are shown in Table 7. As shown in Fig. 6, pseudo-second-order graphs were made by plotting t/q t vs. time. The second-order rate constants have been calculated using the charts. The second order's calculated K 2 , q e , and R 2 are supported by Table 7.
Pseudo-second-order kinetics' correlation coefficients are becoming close to unity in contrast to pseudofirst-order kinetics. Therefore, it is evident that the pseudo-second-order model represents a biosorption that is more successful.
Thermodynamics study. As expected, the biosorption capacity of MB onto T. aestivum increases substantially while the temperature rises from 20 to 40 °C. The biosorption capability of T. aestivum is boosted through the biosorbent's expanded pore length and the warming of the sorbent's surface. Raising the temperature causes the  www.nature.com/scientificreports/ big dye molecule to penetrate more deeply, which also enhanced the large dye ion's potency, which lessens the impact of swelling 16,51 . As a result, MB was able to absorb the T. aestivum more quickly at high temperatures. Gibbs free energy ( G), enthalpy ( H), and entropy ( S), among other thermodynamic characteristics, have all been calculated for the extrusion 28 . Furthermore, Table 8 also provides H, G, and S values for 20 mg/L preliminary MB dye concentrations.
The negative values of ΔG demonstrated the spontaneity and viability of the adsorption process for MB sorption on T. aestivum. Because there is less unpredictability at the solid/liquid interface when MB is adsorbing to T. aestivum, the value of entropy ΔS (− 10.11 kJ/mol K) is negative. The negative value of ΔH (− 12,300.04 kJ/  Figure 6. Pseudo second order kinetic curve for MB elimination% by T. aestivum. Table 8. Parameters for the thermodynamic removal of MB from T. aestivum. www.nature.com/scientificreports/ mol for MB) supports the exothermic character of the reaction. Good interaction between T. aestivum and MB is indicated by high levels of ΔH. This led us to the conclusion that the sorption of the dye in T. aestivum is a process of chemical biosorption.

288
Sticking probability. The sticking probability (S*) is a function of the adsorbate/biosorbent system under discussion, but it is temperature dependent and needs to fulfil the criterion 1 < S* < 1 for optimum biosorption. The value of sticking probability was calculated from experimental data. It was calculated using a modified Arrhenius-type equation.
The parameter S* represents the measure of an adsorbate's capability to persist on the adsorbent indefinitely. The surface coverage ( θ ) at different temperatures was calculated to assess the effects of temperature on the sticking probability over the temperature range from 288 to 308 K. The slope and intercept of the ln (1 − ϴ) against 1/T plot can be used to determine the value of Ea and S*. The negative value of Ea shows that methylene blue dye removal by adsorption onto Triticum aestivum is favoured by a lower solution temperature, and the biosorption process is exothermic in nature. This biosorbent has affinity for methylene blue, indicating that it is a superior biosorbent for removal of methylene blue, as shown by MB sticking probability of S* < 1 on the surface of biomass is presented in Table 8.
Artificial neural networks (ANNs) modelling. ANNs are used to generate new processes, analyse existing ones, and anticipate the result and performance of systems 26 . The feed-forward neural network's optimal topology consists of an output layer, a hidden layer, and four neurons each in the input and hidden layers (including one neuron).
The experiments designed by the CCD provided the input and output for training. After training, a neural network's weights and biases are displayed in Table 9. The model's logsig (log-sigmoid) transfer function provides the necessary information for anticipating the outcomes. Figure 7 displays the expected values of the ANN model. In terms of the number of learning epochs, Fig. 8 analyses the ANN model's training, validation, and tests.
The RSM-predicted improved process conditions are also assessed using an ANN model. Biosorbent dose (2 mg), dye concentration (20 mg/l), dye solution pH (7) and temperature (20 °C) are used as input parameters for the ANN model. When the test error is lowest and the mean squared error has not changed for at least 1000 iterations, the training is terminated. The network is trained in this analysis for a total of 6 epochs. When the Table 9. Network weights and biases using the optimal parameters for the ANN model.  (Table 9).
Multiple response optimization. The experimental findings were optimised using Design-Expert software 35 . The 93.51% biosorption efficiency was attained under the ideal conditions shown in Table 10 (Table 10).
Characterization of biosorbent. Surface modification analysis by FTIR. Using FTIR spectroscopy, surface alteration may be found, allowing the biosorption mechanism to be examined. Using the Perkin Elmer FTIR system, the FTIR spectrum data was Gathered. The surface of the biosorbent is visible with functional groups such as nitro, hydroxyl, carbonyl, carboxylic, phenol, and phenol groups in Fig. 9. FTIR spectra can be used to distinguish between the many functional groups that are present in biosorbent formations 52 . Two distinct peaks at 1372 and 1371 cm −1 and 1512 and 1511 cm −1 , respectively, indicate the stretching vibration of the nitro-N-O groups, which were discovered to have been extended due to biosorption on biosorbent. The 1634 and 1632 cm −1 peaks are the stretches of C=C. The C-O stretch of various moieties and the carboxylic group have been implicated as the cause of the numerous strong, sharp peaks that were observed between the levels of 1100 and 1330 cm −124 . The hydroxyl functional group's O-H stretching vibration and the band at 3200-3600 cm −1 had previously been linked (Fig. 9). The stretching of the carboxyl groups in C=O is responsible for the peak around   www.nature.com/scientificreports/ 1700-1800 cm −1 . The surface charge differential may change due to positive or negative surface charges depending on the pH of the solution 2,6,53 . In a solution with a lower pH value, the system will operate more frequently and develop a positive surface charge. The hydroxyl group is indicated by the height increase at 3340 cm −1 caused by the MB absorbed on T. aestivum, as well as by the prolonged robust sharp top at 1034 cm −1 . Peaks in the range of 1327-1372 cm −1 were caused by the interaction of MB and Nitro companies in T. aestivum [54][55][56] .
SEM evaluation. The surface topography and properties of T. aestivum can be directly scanned using a scanning electron microscope (SEM) examination. SEM images are displayed Before and after MB biosorption, the biomass of T. aestivum (Fig. 10A,B). The biomass made of untreated T. aestivum had a rough and irregular surface, as seen in Fig. 10A. The look of fresh, shining particles absorbed on the surface of T. aestivum was depicted in Fig. 10B. Another distinguishing quality had been demonstrated (Fig. 10B). The surface area of polymeric T. aestivum has been reduced due to possible cross-linking between positively charged ions and negatively charged chemical functional groups in the cell wall. The surface of T. aestivum is rough and undulated, which increases the surface area exposure of the active biosorption sites and leads to MB's enhanced bio-absorption efficacy.

Conclusion
The biosorption of MB dye onto T. aestivum was demonstrated in this work using the experimental variables of biosorbent quantity, dye pH, temperature, and concentration. The experimental result of the biosorption performance of MB was examined and found to be superior to the use of the CCD-primarily based RSM optimization technique and ANN. Using isotherms, kinetics, and thermodynamics studies, the best RSM results were examined. This study assessed T. aestivum ability to remove MB dye from wastewater. The experimental results were demonstrated to be closely related to the Langmuir isotherm model, which has a maximum biosorption capacity of 0.36 mg/g. Additionally, MB sorption on T. aestivum was studied using pseudo-second-order kinetics at a rate constant of (2.56 gmg −1 min −1 ). Thermodynamic analysis shows that the adsorption process is exothermic and spontaneous. After characterising the biosorbent by evaluation of the T. aestivum FTIR spectra, it was determined that the change of dye ions with counterions, which are typically attached to the surface through carboxyl, hydroxyl, and nitro groups, is the mechanism behind the metal binding. T. aestivum is a more affordable alternative adsorbent even if compared to commercial activated carbon, it has a higher capacity for biosorption. The use of T. aestivum as an adsorbent to take the colour out of water could be inexpensive and efficient.

Data availability
All data generated or analysed during this study are included in this published article.